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对随机组合进行了外推交叉验证
Jin-Hong Du1,2, Pratik Patil3, Kathryn Roeder1
1Department of Statistics and Data Science, Carnegie Mellon University.
Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|October 23, 2024
概括
外推交叉验证 (ECV) 有效地调整随机组合参数,如组合和子样本大小. 与传统的交叉验证技术相比,这种新的方法实现了接近最佳的预测准确性,计算成本较低.
科学领域:
- 机器学习 机器学习
- 计算生物学 计算生物学
- 统计建模 统计建模
背景情况:
- 集体方法,包括包装和随机森林,广泛应用于各种科学领域.
- 集成参数的高效调整仍然是一个重大挑战,尽管它们的流行.
- 现有的交叉验证方法可能是计算密集型或对参数调整不理想的.
研究的目的:
- 引入额外推算交叉验证 (ECV) 以优化随机组合中的集体和子样本大小.
- 开发一种在参数调节中实现高精度和计算效率的方法.
- 解决对高维数据和计算约束的有效调整策略的需求.
主要方法:
- 在小集成尺寸的初始估计器中使用袋外误差.
- 采用一种基于预测风险分解的新风险推断技术.
- 建立组合和子样本大小的风险推断的统一一致性.
主要成果:
- 对于二次预测风险,ECV产生了-optimal组合,接近预言调整的性能.
- 该方法在包括高维设置在内的各种集合和子样本大小中证明了理论一致性.
- 在一个预测表面蛋白质丰度的案例研究中,ECV的表现优于样本分割和k折交叉验证.
结论:
- ECV提供了一种计算效率高,准确的方法来调随机组合.
- 该方法在理论上很强大,可以适应一般预测因素和轻微时刻假设.
- 在计算约束下,ECV为复杂的生物数据分析中的参数优化提供了实用解决方案.
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